Crop Identification by Using Seasonal Parameters Extracted from Time Series Landsat Images in a Mountainous Agricultural County of Eastern Qinghai Province, China
Bibliographic record
Abstract
Time series vegetable indexes (Vis) have been evidenced a useful data to extract vegetable phenology and identify crop types. This paper conducted such a research in Qinghai Province by using Landsat TM images, via four steps, i) sampling single-crop plots and extracting crop spectrums based on pure signle-crop pixels; ii) building time-series vegetable indexes by using Landsat 8 TM images (2013-2014); iii) extracting seasonal parameters according to algorithms defined in TIMESAT program; vi) generating a decision tree for identifying crop types and validate classification accuracy via ground investigation. The results indicate that crops planted in a larger continuous range, such as spring wheat, potato and rapeseed, achieved an acceptable accuracy of above 70%, while crops planted too dispersedly (like broad bean, which is often inter-planted with other crops) or with a too smaller planting range (like barley), remained a poor recognition rates (below 50%). The value of this work lies in it displayed not only the classification accuracy of crop types in this region by using such methodology, but also the feasibility of integrating VIs calculation, seasonal parameter extracting and decision tree generation into one computer program, which is highly desired in this region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".